Customer Theory & Buyer Personas

Customer Theory is a document that indicates basic Customer data i.e. what’s known or what we think we know about different types of users we have. 

It details every note-worthy detail we are learning about our customers. So every time you run a test that we can learn from, you'll update that document. Each new test is planned by putting into account what is already known and captured about them.

This greatly improves our resilience in tackling clear issues, challenges, or any difficulty that potential Customers might face in their interactions with us.

“The goal of a test is not to get a lift, but rather to get a learning.” – Dr. Flint McGlaughlin, Managing Director and CEO, MECLABS

Many marketers have latched onto A/B testing as a way to improve marketing results. And, they can certainly do that.

However, to really drive sustainable returns, you must look past a test that simply tells you to use the red button instead of the blue button, and instead see what split testing is teaching you about your customers.

Knowing enough to predict

The Customer Theory is an understanding of the customer that enables us to more accurately predict the total response to a given offer.

In an era of big data, it can be overwhelming to manage results, metrics, and numbers. Understanding Customer Theory focuses solely on the information that teaches marketers about the customer decision-making process, allowing firms to more accurately predict buyer behavior, without being bogged down by superfluous data.

When you get a lift – great! 

However, identifying specifically what, how, and why these changes made an impact is even better. If you understand what made the difference, you can repeat that in the next tests and dramatically increase your chances of getting a lift next time around.

Discoveries are invaluable in informing future testing. Discoveries about your customer – additions to your customer theory – are important because they can be attained regardless of whether your treatments “win” or “lose”. Even better – when your goal is learning, you’re always winning since you’re always learning.

Customer theory is essentially what you currently think you know about the target audience (well-documented!). The purpose of customer theory is to have an understanding of the customer that enables us to more accurately predict response to treatment, or what the treatment should be, to begin with.

Customer theory consists of buyer personas (people we’re trying to sell to) and overall documentation of what has worked, what hasn’t, and what might work. An added benefit is that you can bring in new team members to work on a CRO project and get them up to speed fast by sharing your customer theory.

Buyer personas

Buyer personas (also called avatars) is a sub-set of customer theory. They’re fictional representations of your customers, based on real data about customers and their online behavior, along with educated speculation about their intent, motivations, and concerns.

How you should treat personas: a clear understanding of a target customer that exists in the minds of your team. When optimizing a site, you always need to keep the personas in mind. Put yourself in the shoes of your customer.

Buyer personas have been around for general marketing purposes since the 1990s, and are often demographics-based (and often based on ideas, not data). Traditionally they’re often based on demographics (age, sex, income, etc), but in this day and age demographics can lie to you. They often do.

It’s often more about where they’re at in their life or the problem they’re trying to solve than age or gender. Age and gender might give us an idea of their technical savvy and possible communication styles. The business role can be important for B2B. But most of it is typically useless. Unless you’re a dating service or wedding organizer, demographics like marital status don’t make any difference.

Be very skeptical about the demographic data that you need for your personas. Often, most business people are mainly interested in the following three characteristics:

1. Intent (motivation, goals):

• Why are they coming to this site at this time?
• What are they looking to accomplish?
• Where are they arriving, what information did they see before landing on your site?

2. Concerns and fears (friction):

• What are their fears, doubts, hesitations?
• What are the important pieces of information this kind of visitor needs to feel comfortable and confident in taking action?

3. Mode of Persuasion:

• Will they decide to take action quickly or slowly?
• Will they seek to decide mainly emotionally (e.g. when buying a pair of pants) or logically (e.g. when looking for a new SSD hard drive)?

How to Run Tests

Testing is a key part of conversion optimization. It’s the only way to validate a hypothesis, to know what’s really working. Until you test something, you’re only guessing.

Testing is a key part of conversion optimization. It’s the only way to validate a hypothesis, to know what’s really working. Until you test something, you’re only guessing.

You should not implement any website change based on the way looks alone. Or worse yet – you shouldn’t change anything to please your boss or client. It’s impossible to know/predict the impact of a design change in advance. Anyone telling you otherwise is just full of themselves, or clueless.

Testing is the only way to ensure that every change produces positive results. Quantitative data speaks for itself. You need to be measuring the impact that changes have on your metrics such as sign-ups, downloads, purchases, or whatever else your goals may be.

Constantly testing and optimizing your page will increase revenue, signups or any other key metrics – while providing you with valuable insight about your target audience (you can improve your customer theory with each test).

What about just changing something, and seeing if the conversion rate will go up or down?

While this is called sequential testing, it’s actually not testing. It’s not apples to apple comparison since it’s not the same traffic nor the same market conditions. 

Your conversion rate is not a fixed number, it fluctuates daily and monthly. It’s going to be different for each traffic source. Your traffic sources might be mostly the same week to week, but the exact distribution can vary greatly. So if you measure results by displaying Version A for one week and then Version B for one week, it won’t be an accurate comparison. 

Comparison is gonna be even more off when you run paid traffic campaigns (offline or online), or your testing period is affected by an external event like Christmas, Mother’s day, beautiful weather, political unrest, scandals in the media, or stuff your competitors do. You can never be sure which external events affect your conversion rates.

Also: People's browsing habits differ, and analyzing the performance of version A on a Thursday evening vs the performance of version B on a Saturday morning will result in the data being unusable. Any number of other variables could be affecting the test. Split your traffic and test at the same time.

You can’t trust the results of sequential testing.

A/B testing and multivariate testing

Largely speaking there are 2 types of testing: A/B/n testing and multivariate testing. 

A/B/n testing

A/B testing (or ‘split testing’) is when you create two versions of a page (page A and page B). 50% of the traffic is shown on page A, and the other 50% is taken to page B.

This division is done automatically by a split testing software (e.g. Optimizely, Visual Website Optimizer, etc).

If a user lands on page A, a cookie is placed on her computer, so that when she comes back later, she will always see version A. This ensures that people won’t really notice that you’re conducting any testing on your website.

(Of course, if they delete cookies or switch browsers/devices/computers they can see a different variation – but that’s gonna be such a small sample size that you shouldn’t be concerned by this at all).

When we’re talking about A/B testing, we’re always actually talking about A/B/n testing (e.g. A/B/C/D testing). The more versions you test at the same time, the more time it takes for you to know which one is the best. Speed of testing is also important, so if you have low traffic (e.g. less than 30k / mo), skip the Cs and Ds.

If you test more than two pages against each other, it will take you more time to find the winner. 

Multivariate testing

Multivariate testing enables you to test more than 2 combinations at the same time, and the combination of different combinations. Let me explain.

Let’s say you’re testing 2 versions of a headline, 2 versions of a call to action text on a button, and 3 different images of the page at the same time (as on the picture above).

So the winning combination could be:

• headline 1, button 2, image 1
• headline 2, button 1, image 3
• headline 1, button 1, image 2

… etc. Lots of possibilities, and you’d need a lot of traffic to find the winning combination.

Only do this if you have a ton of traffic. Low modest traffic websites should stick to A/B testing.

Customer Theory:

Why name all your Personas? 

The idea here is that by giving your persona a name (and a specific age) you’ll make it easier for you and your team to picture them as a real person. So when writing copy or deciding over layouts, you can think “what will they want to see” or “what information do they want from your Copy?”

Important: the above constitutes your basic Customer Theory.

• It’s what you think you know about your clients.
• Each time you run a test that follows a certain hypothesis, revise it + add a note which hypothesis was tested, and what was the outcome.

Improve customer theory with every test

Your Customer Theory is never complete, and you have to improve it with every single test by applying the insight, learning you got from test results.

Insight and update to customer theory: A simple, straightforward approach works best for this audience. So the question is—how can we use this insight to make the page even simpler? Building on the “simple” insight we got from the previous test, we updated our customer theory and created a shorter, simpler version of the page:

Insight and update to customer theory

Always glean key insights, lessons, and learnings from previous tests...

Constantly updating your customer theory and basing your test hypotheses on them will result in big wins for your business, stakeholders, and customers as well.

When you start testing a page, don’t test just once and move on to testing other parts of the site. Don’t think of the process as one-off tests, but as testing campaigns.

Learn from each test, make sure you send test data to Google Analytics and segment the results (dive deep into details with this exercise, spare no effort), and remember to keep iterating.

Use insights from previous tests to drive upcoming tests. You won’t know what matters until you test it.

Have a lot of patience!

You'll see even what cannot be loosely described as the 'low lying' fruits for your business.